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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of COMPUTER ENGINEERING
Computer Engineering, Masters with Thesis
Course Catalog
http://ceng.ktu.edu.tr
Phone: +90 0462 3773157
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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of COMPUTER ENGINEERING / Computer Engineering, Masters with Thesis
Katalog Ana Sayfa
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BIL5040Computer Vision3+0+0ECTS:7.5
Year / SemesterSpring Semester
Level of CourseSecond Cycle
Status Elective
DepartmentDEPARTMENT of COMPUTER ENGINEERING
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 3 hours of lectures per week
LecturerProf. Dr. Murat EKİNCİ
Co-LecturerNone
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
The principal objectives of this course continue to be to provide an introduction to basic concepts and methodologies for computer vision, and to develop a foundation that can be used as the basis for further study and research in this field.
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : provide an introduction to basic concepts and methodologies for image processing and computer vision,1,3,4,5,8,10,111
PO - 2 : develop a foundation that can be used as the basis for further study and research in this field. 1,2,3,4,5,8,10,11,14,151,2
PO - 3 : achieve simple algorithms for different pattern recognition research1,3,4,5,8,10,11,13,14,151,2
PO - 4 : create computer vision based approach for different research in other disciplines1,4,7,8,10,11,14,152
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome

 
Contents of the Course
Image Pre-processing; Local pre-processing, and edge detectors; Thresholding based image segmentation; Edge-based, region merging-splitting based image segmentation; Mathing methods; Texture feature extraction and statistical texture recognition; Feature Classification; Shape representation and description; Feature extraction and statistical pattern recognition; Image classification and understanding; Mathematical morpholgy; Vision geometry and 3D vision; Motion analysis.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Image Pre-processing
 Week 2Local pre-processing, and edge detectors
 Week 3Thresholding based image segmentation
 Week 4Edge-based, region merging-splitting based image segmentation;
 Week 5Segmentation as Clustering and Matching methods,
 Week 6Texture feature extraction and statistical texture recognition,
 Week 7Basic Feature Classification
 Week 8Shape representation and description
 Week 9Mid-term exam
 Week 10Feature extraction and statistical pattern recognition
 Week 11Image classification and understanding,
 Week 12Mathematical morpholgy
 Week 13Vision geometry and 3D vision
 Week 14Principal Component Analysis and Fisher Discrement Analysis in Pattern Recognition
 Week 15Pattern Classification Algorithms
 Week 16End-of-term exam
 
Textbook / Material
1Milan Sonka, Vaclav Hlavac, Roger Boyle, 1999, Image Processing, Analysis, and Machine Vision, Second Edition, PWS Puıblishing,
 
Recommended Reading
1Rafael C. Gonzales, Richard E. Woods, 1998, Digital Image Processing, Addison-Wesley Publishing Company
2Gerhard X. Ritter, Joseph N. Wilson, 2001, Handbook of Computer Vision Algorithms in Image Algebra, CRC Press
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 12/04/2013 2 30
Project 15 24/05/2013 2 20
End-of-term exam 16 07/06/2013 2 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 4 14 56
Arasınav için hazırlık 12 1 12
Arasınav 2 1 2
Proje 5 14 70
Dönem sonu sınavı için hazırlık 15 1 15
Dönem sonu sınavı 3 1 3
Total work load200